Key brain mechanisms for organizing memories in time
Convergence research project integrated neurobiology with data science techniques

Date:
February 15, 2022
Source:
University of California - Irvine
Summary:
Using experiments and a deep machine learning data analysis
approach, scientists uncovered the fundamental workings of the
hippocampus region of the brain as it organizes memories into
time sequences. The work could help future research into cognitive
disorders such as Alzheimer's disease and other causes of dementia.



FULL STORY ==========================================================================
In a scientific first, researchers at the University of California,
Irvine have discovered fundamental mechanisms by which the hippocampus
region of the brain organizes memories into sequences and how this can
be used to plan future behavior. The finding may be a critical early
step toward understanding memory failures in cognitive disorders such
as Alzheimer's disease and other forms of dementia.


========================================================================== Combining electrophysiological recording techniques in rodents with
a statistical machine learning analysis of huge troves of data, the
UCI researchers uncovered evidence suggesting that the hippocampal
network encodes and preserves progressions of experiences to aid in decision-making. The team's work is the subject of a paper published
recently in Nature Communications.

"Our brain keeps a pretty good record of when specific experiences
or events occur. This ability helps us function in our daily life,
but before this study, we didn't have a clear idea of the neuronal
mechanisms behind these processes," said corresponding author Norbert
Fortin, UCI associate professor of neurobiology and behavior. "Where
it connects with everybody is that this type of memory is strongly
impaired in a variety of neurological disorders or simply with aging,
so we really need to know how this brain function works." The project,
which took more than three years to complete, involved experimental and
data analysis phases. The researchers monitored the firing of neurons in
rats' brains as they underwent a series of odor identification tests. By presenting five different smells in various sequences, the scientists
were able to measure the animals' memory of the correct sequence and
detect how their brains captured these sequential relationships.

"The analogy I would think about is computing," Fortin said. "If I were
to stick electrodes in your brain -- we can't; that's why we use rats --
I could see which cells are firing and which ones are not firing at any
given moment.

That provides us with some insight into how the brain represents and
computes information. When we record activity patterns in a structure,
it's like we're seeing zeros and ones in a computer." Obtained in
millisecond intervals over several minutes, neuronal activity and
inactivity measurements present a dynamic picture of the brain's
functioning.

Fortin said that he and his colleagues were, in some ways, able to "read
the minds" of their subjects by viewing the "coding" of the cells --
which ones were firing and which were not -- in rapid succession.



========================================================================== "When you're thinking about something, it moves quickly," he said. "You're
not stuck on that memory for long. Right now, it's being represented,
but we can see how that changes very quickly." Fortin knew early on that
the readings of hippocampal activity would result in enormous quantities
of raw data. From the beginning stages of the project, he enlisted the participation of statisticians in the Donald Bren School of Information & Computer Sciences.

"The neuroscience questions we had at the time in my lab were way too
advanced for the statistical knowledge we had. That's why we needed to
involve partners with data science expertise," Fortin said.

"These emerging neuroscience studies rely on data science methods because
of the complexity of their data," said senior co-author Babak Shahbaba,
UCI Chancellor's Fellow and professor of statistics. "Brain activities are recorded at millisecond scale, and these experiments run for more than an
hour, so you can imagine how fast the amount of data grows. It gets to a
point that neuroscientists need more advanced techniques to accomplish
what they had imagined but weren't able to implement." He noted that
when neurons encode information such as memories, scientists can get
a glimpse of that process by examining the pattern of spiking activity
across all recorded neurons, known collectively as an ensemble.



==========================================================================
"We found that we could treat these neural patterns as images, and
this unlocked our ability to apply deep machine learning methods,"
Shahbaba said.

"We analyzed the data with a convolutional neural network, which is a methodology used frequently in image processing applications such as
facial recognition." This way, the researchers were able to decode the
firing of neurons to retrieve information.

"We know what the signature for odor B looks like, just as we know the
ones for A, C and D," Fortin said. "Because of that, you can see when
those signatures reappear at a different moment in time, such as when
our subjects are anticipating something that has yet to happen. We're
seeing these signatures being quickly replayed as they're thinking about
the future." Shahbaba said that the tools and methodologies developed
during this project can be applied to a wide range of problems, and
Fortin may extend his line of inquiry into other brain regions.

The study is an example of the power of convergence research at
institutions such as UCI, Shahbaba said: "I could directly see the
difference this is making for our students. Researchers in Norbert's neuroscience group are taking data science classes and can now ask some
really important scientific questions they could not investigate in the
past, and my own students are thinking fundamentally about the scientific method in an unprecedented way." He added, "Through this collaboration,
we are training the next generation of scientists, who have the required
skills to conduct interdisciplinary research." Fortin and Shahbaba were
joined on the project by Pierre Baldi, UCI Distinguished Professor of
computer science; Lingge Li, who earned a Ph.D. in statistics at UCI
in 2020; Forest Agostinelli, who earned a Ph.D. in computer science
at UCI in 2019 and is now an assistant professor at the University of
South Carolina; Mansi Saraf and Keiland Cooper, UCI Ph.D. students in neurobiology and behavior; Derenik Haghverdian, a UCI Ph.D. student
in statistics; and Gabriel Elias, a postdoctoral project scientist at
UCI. Funding was provided by the National Institutes of Health, the
National Science Foundation and the Whitehall Foundation.

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by
University_of_California_-_Irvine. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Babak Shahbaba, Lingge Li, Forest Agostinelli, Mansi Saraf,
Keiland W.

Cooper, Derenik Haghverdian, Gabriel A. Elias, Pierre Baldi,
Norbert J.

Fortin. Hippocampal ensembles represent sequential relationships
among an extended sequence of nonspatial events. Nature
Communications, 2022; 13 (1) DOI: 10.1038/s41467-022-28057-6 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220215163418.htm

--- up 10 weeks, 3 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)